冻融环境下玄武岩纤维增强泡沫混凝土的单轴压缩特性  

Uniaxial Compression Characteristics of Basalt Fiber Reinforced Foam Concrete in Freeze-Thaw Environments

作　　者：吕国旭 陈波[1,2] 周程涛 詹明强 LYU Guoxu;CHEN Bo;ZHOU Chengtao;ZHAN Mingqiang(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;State Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China;Fujian Shuikou Power Generation Group Co.,Ltd.,Fuzhou 350001,China)

机构地区：[1]河海大学水利水电学院,南京210098 [2]河海大学水灾害防御全国重点实验室,南京210098 [3]福建水口发电集团有限公司,福州350001

出　　处：《三峡大学学报（自然科学版）》2025年第1期1-8,共8页Journal of China Three Gorges University：Natural Sciences

基　　金：国家自然科学基金面上项目(52079049);国家重点实验室基本科研业务费专项资金资助项目(522012272,5230248A2)。

摘　　要：为了研究密度、纤维掺量及冻融循环对玄武岩纤维增强泡沫混凝土单轴压缩特性的影响,借助X-CT技术以及Avizo软件对试件的孔隙构造进行了研究,对不同冻融环境下的多种密度等级(800和1000kg/m3)及纤维体积掺量(0%、0.15%、0.30%、0.45%)的玄武岩纤维增强泡沫混凝土进行了单轴压缩试验,分析了不同影响因素下玄武岩纤维增强泡沫混凝土的单轴压缩特性,对各因素与抗压强度的关系进行了灰关联度分析,并使用机器学习算法模型对抗压强度进行预测.结果表明:掺入玄武岩纤维能有效提高泡沫混凝土抵抗冻融的能力,体积掺入0.15%、0.30%、0.45%的试件冻融80次后强度损失率分别为75.2%、46.2%、37.8%.密度等级与抗压强度关联度最大,灰关联度为0.799;其次是纤维掺量,关联系数为0.723;灰狼算法优化下的长短期记忆网络模型对玄武岩纤维增强泡沫混凝土抗压强度的预测性能良好.To study the effects of density,fiber content,and freeze-thaw cycles on the uniaxial compressive properties of basalt fiber reinforced foam concrete(BFRFC),the pore structure of the specimens was examined by X-CT technology and analyzed by Avizo software.Uniaxial compression tests for BFRFC samples were conducted with various density levels(i.e.,800 kg/m3 and 1000 kg/m3)and fiber contents(i.e.,0%,0.15%,0.30%,0.45%)under different freeze-thaw environments.The uniaxial compressive properties of BFRFC under different influencing factors were analyzed,and the relationship between each factor and compressive strength was assessed using grey relational analysis.A machine learning algorithm model was employed to predict compressive strength.The results indicate that the incorporation of basalt fibers effectively enhances the freeze-thaw resistance of foamed concrete.The strength loss rates after 80 freeze-thaw cycles for specimens with 0.15%,0.30%,and 0.45%fiber content were 75.2%,46.2%,and 37.8%,respectively.The density level has the highest correlation with compressive strength,with the grey relational degree of 0.799,followed by fiber content with the correlation coefficient of 0.723.The long shortterm memory network model optimized by the grey wolf algorithm demonstrates good predictive performance for the compressive strength of basalt fiber reinforced foam concrete.

关 键 词：玄武岩纤维增强泡沫混凝土 单轴压缩 冻融循环 X-CT 灰关联度 长短期记忆网络 灰狼优化算法 

分 类 号：TU528.2[建筑科学—建筑技术科学]